Table 3 Expert information.

From: A novel decision-making approach for the selection of best deep learning techniques under logarithmic fractional fuzzy set information

Alternatives

\(k_{1}\)

\(k_{2}\)

\(k_{3}\)

\(k_{4}\)

\(k_{5}\)

\(h_{1}\)

\(\left\langle 0.02,0.07\right\rangle\)

\(\left\langle 0.02,0.08\right\rangle\)

\(\left\langle 0.03,0.09\right\rangle\)

\(\left\langle 0.02,0.06\right\rangle\)

\(\left\langle 0.03,0.09\right\rangle\)

\(h_{2}\)

\(\left\langle 0.03,0.08\right\rangle\)

\(\left\langle 0.02,0.06\right\rangle\)

\(\left\langle 0.01,0.07\right\rangle\)

\(\left\langle 0.03,0.05\right\rangle\)

\(\left\langle 0.02,0.07\right\rangle\)

\(h_{3}\)

\(\left\langle 0.02,0.05\right\rangle\)

\(\left\langle 0.01,0.08\right\rangle\)

\(\left\langle 0.03,0.06\right\rangle\)

\(\left\langle 0.05,0.06\right\rangle\)

\(\left\langle 0.05,0.06\right\rangle\)

\(h_{4}\)

\(\left\langle 0.02,0.08\right\rangle\)

\(\left\langle 0.03,0.08\right\rangle\)

\(\left\langle 0.03,0.05\right\rangle\)

\(\left\langle 0.03,0.08\right\rangle\)

\(\left\langle 0.02,0.06\right\rangle\)

\(h_{5}\)

\(\left\langle 0.01,0.06\right\rangle\)

\(\left\langle 0.01,0.06\right\rangle\)

\(\left\langle 0.01,0.07\right\rangle\)

\(\left\langle 0.02,0.09\right\rangle\)

\(\left\langle 0.03,0.08\right\rangle\)